Math proof allegedly developed by GPT-5.6 sets new optimization convergence bounds
Dr_Singularity · x · 2026-08-17
Researchers Jianhao Ma and Yuxin Chen report a new theoretical result in optimization, proving that tuning gradient descent step sizes cannot achieve the optimal O(T^−2) convergence rate and establishing a new lower bound around Ω(T^−1.9319).
The main proof was reportedly developed by GPT-5.6 Sol Pro. The researchers state they supplied the research objective and high-level strategy, but none of the nontrivial mathematical ingredients. The argument emerged after AI iterations, was verified by humans, and formalized in Lean 4 using Codex.
Related event: GPT-5.6 Helps Prove New Lower Bound for Gradient Descent(2 posts)→
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